Hierarchy and extremes in selections from pools of randomized proteins

Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2016-03, Vol.113 (13), p.3482-3487
Main Authors: Boyer, Sébastien, Biswas, Dipanwita, Soshee, Ananda Kumar, Scaramozzino, Natale, Nizak, Clément, Rivoire, Olivier
Format: Article
Language:English
Subjects:
Amino Acid Sequence
Animals
Biological Sciences
Cell Surface Display Techniques
Directed Molecular Evolution - methods
Directed Molecular Evolution - statistics & numerical data
Escherichia coli - genetics
Evolution
High-Throughput Nucleotide Sequencing
Humans
Life Sciences
Models, Statistical
Molecular Sequence Data
Peptide Library
Physical Sciences
Probability
Proteins
Proteins - chemistry
Proteins - genetics
Reproducibility of Results
Sequence Alignment
Statistical analysis
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Summary:Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different “frameworks” typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution).
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1517813113